






'''simple docstring'''





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)
def __UpperCAmelCase   (					A					:			List[Any]			,       A					:			List[str]=False		)    ->   Dict:
			UpperCAmelCase_      :       Optional[int]          =       []
			for i in range(config.num_hidden_layers		):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((F"module.blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight")		)
						rename_keys.append((F"module.blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")		)
						rename_keys.append(
						    (F"module.blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")		)
						rename_keys.append((F"module.blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")		)
						rename_keys.append((F"module.blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")		)
						rename_keys.append((F"module.blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")		)
						rename_keys.append((F"module.blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")		)
						rename_keys.append((F"module.blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")		)
						rename_keys.append((F"module.blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")		)
						rename_keys.append((F"module.blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")		)

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        ('''module.cls_token''', '''vit.embeddings.cls_token'''),
			        ('''module.patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
			        ('''module.patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
			        ('''module.pos_embed''', '''vit.embeddings.position_embeddings'''),
			    ]		)

			if base_model:
						# layernorm + pooler
						rename_keys.extend(
						    [
						        ('''module.norm.weight''', '''layernorm.weight'''),
						        ('''module.norm.bias''', '''layernorm.bias'''),
						    ]		)

						# if just the base model, we should remove "vit" from all keys that start with "vit"
						UpperCAmelCase_      :       Union[str, Any]          =       [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''		) else pair for pair in rename_keys]
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ('''norm.weight''', '''vit.layernorm.weight'''),
						        ('''norm.bias''', '''vit.layernorm.bias'''),
						        ('''head.weight''', '''classifier.weight'''),
						        ('''head.bias''', '''classifier.bias'''),
						    ]		)

			return rename_keys
def __UpperCAmelCase   (					A					:			Any			,       A					:			List[Any]			,       A					:			Dict=False		)    ->   Optional[int]:
			for i in range(config.num_hidden_layers		):
						if base_model:
									UpperCAmelCase_      :       Union[str, Any]          =       ''''''
						else:
									UpperCAmelCase_      :       int          =       '''vit.'''
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						UpperCAmelCase_      :       List[Any]          =       state_dict.pop(F"module.blocks.{i}.attn.qkv.weight"		)
						UpperCAmelCase_      :       Optional[Any]          =       state_dict.pop(F"module.blocks.{i}.attn.qkv.bias"		)
						# next, add query, keys and values (in that order) to the state dict
						UpperCAmelCase_      :       int          =       in_proj_weight[
						    : config.hidden_size, :
						]
						UpperCAmelCase_      :       Any          =       in_proj_bias[: config.hidden_size]
						UpperCAmelCase_      :       List[Any]          =       in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCAmelCase_      :       int          =       in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						UpperCAmelCase_      :       int          =       in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCAmelCase_      :       Dict          =       in_proj_bias[-config.hidden_size :]
def __UpperCAmelCase   (					A					:			Optional[int]		)    ->   Any:
			UpperCAmelCase_      :       Dict          =       ['''head.weight''', '''head.bias''']
			for k in ignore_keys:
						state_dict.pop(a_			,       a_		)
def __UpperCAmelCase   (					A					:			Tuple		)    ->   Optional[int]:
			# projection head is used in the self-supervised pre-training in MSN,
			# for downstream task it's not needed.
			UpperCAmelCase_      :       Union[str, Any]          =       [
			    '''module.fc.fc1.weight''',
			    '''module.fc.fc1.bias''',
			    '''module.fc.bn1.weight''',
			    '''module.fc.bn1.bias''',
			    '''module.fc.bn1.running_mean''',
			    '''module.fc.bn1.running_var''',
			    '''module.fc.bn1.num_batches_tracked''',
			    '''module.fc.fc2.weight''',
			    '''module.fc.fc2.bias''',
			    '''module.fc.bn2.weight''',
			    '''module.fc.bn2.bias''',
			    '''module.fc.bn2.running_mean''',
			    '''module.fc.bn2.running_var''',
			    '''module.fc.bn2.num_batches_tracked''',
			    '''module.fc.fc3.weight''',
			    '''module.fc.fc3.bias''',
			]
			for k in ignore_keys:
						state_dict.pop(a_			,       a_		)
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			str			,       A					:			List[Any]		)    ->   Optional[Any]:
			UpperCAmelCase_      :       Optional[Any]          =       dct.pop(a_		)
			UpperCAmelCase_      :       Tuple          =       val
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			List[str]		)    ->   Tuple:
			UpperCAmelCase_      :       Optional[int]          =       ViTMSNConfig()
			UpperCAmelCase_      :       Tuple          =       1_0_0_0

			UpperCAmelCase_      :       Union[str, Any]          =       '''datasets/huggingface/label-files'''
			UpperCAmelCase_      :       List[str]          =       '''imagenet-1k-id2label.json'''
			UpperCAmelCase_      :       Optional[Any]          =       json.load(open(hf_hub_download(a_			,       a_		)			,       '''r'''		)		)
			UpperCAmelCase_      :       Union[str, Any]          =       {int(a_		): v for k, v in idalabel.items()}
			UpperCAmelCase_      :       List[Any]          =       idalabel
			UpperCAmelCase_      :       List[Any]          =       {v: k for k, v in idalabel.items()}

			if "s16" in checkpoint_url:
						UpperCAmelCase_      :       Optional[Any]          =       3_8_4
						UpperCAmelCase_      :       int          =       1_5_3_6
						UpperCAmelCase_      :       Dict          =       6
			elif "l16" in checkpoint_url:
						UpperCAmelCase_      :       int          =       1_0_2_4
						UpperCAmelCase_      :       Union[str, Any]          =       4_0_9_6
						UpperCAmelCase_      :       Tuple          =       2_4
						UpperCAmelCase_      :       Any          =       1_6
						UpperCAmelCase_      :       Tuple          =       0.1
			elif "b4" in checkpoint_url:
						UpperCAmelCase_      :       Any          =       4
			elif "l7" in checkpoint_url:
						UpperCAmelCase_      :       Tuple          =       7
						UpperCAmelCase_      :       List[str]          =       1_0_2_4
						UpperCAmelCase_      :       Dict          =       4_0_9_6
						UpperCAmelCase_      :       List[str]          =       2_4
						UpperCAmelCase_      :       List[str]          =       1_6
						UpperCAmelCase_      :       Union[str, Any]          =       0.1

			UpperCAmelCase_      :       int          =       ViTMSNModel(a_		)

			UpperCAmelCase_      :       List[Any]          =       torch.hub.load_state_dict_from_url(a_			,       map_location='''cpu'''		)['''target_encoder''']

			UpperCAmelCase_      :       Tuple          =       ViTImageProcessor(size=config.image_size		)

			remove_projection_head(a_		)
			UpperCAmelCase_      :       List[Any]          =       create_rename_keys(a_			,       base_model=a_		)

			for src, dest in rename_keys:
						rename_key(a_			,       a_			,       a_		)
			read_in_q_k_v(a_			,       a_			,       base_model=a_		)

			model.load_state_dict(a_		)
			model.eval()

			UpperCAmelCase_      :       int          =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''

			UpperCAmelCase_      :       List[str]          =       Image.open(requests.get(a_			,       stream=a_		).raw		)
			UpperCAmelCase_      :       Tuple          =       ViTImageProcessor(
			    size=config.image_size			,       image_mean=a_			,       image_std=a_		)
			UpperCAmelCase_      :       Optional[int]          =       image_processor(images=a_			,       return_tensors='''pt'''		)

			# forward pass
			torch.manual_seed(2		)
			UpperCAmelCase_      :       str          =       model(**a_		)
			UpperCAmelCase_      :       List[Any]          =       outputs.last_hidden_state

			# The following Colab Notebook was used to generate these outputs:
			# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
			if "s16" in checkpoint_url:
						UpperCAmelCase_      :       str          =       torch.tensor([[-1.0915, -1.4876, -1.1809]]		)
			elif "b16" in checkpoint_url:
						UpperCAmelCase_      :       List[Any]          =       torch.tensor([[14.2889, -18.9045, 11.7281]]		)
			elif "l16" in checkpoint_url:
						UpperCAmelCase_      :       int          =       torch.tensor([[41.5028, -22.8681, 45.6475]]		)
			elif "b4" in checkpoint_url:
						UpperCAmelCase_      :       Optional[Any]          =       torch.tensor([[-4.3868, 5.2932, -0.4137]]		)
			else:
						UpperCAmelCase_      :       str          =       torch.tensor([[-0.1792, -0.6465, 2.4263]]		)

			# verify logits
			assert torch.allclose(last_hidden_state[:, 0, :3]			,       a_			,       atol=1e-4		)

			print(F"Saving model to {pytorch_dump_folder_path}"		)
			model.save_pretrained(a_		)

			print(F"Saving image processor to {pytorch_dump_folder_path}"		)
			image_processor.save_pretrained(a_		)


if __name__ == "__main__":
						_UpperCamelCase						:						Dict  =					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint_url',
						    default='https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar',
						    type=str,
						    help='URL of the checkpoint you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)

						_UpperCamelCase						:						Union[str, Any]  =					parser.parse_args()
						convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








'''simple docstring'''


class  __lowercase    :  # Public class to implement a graph







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     __a								=       row
     __a								=       col
     __a								=       graph







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     bool:
     return (
         0 <= i < self.ROW
         and 0 <= j < self.COL
         and not visited[i][j]
         and self.graph[i][j]
     )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     # Checking all 8 elements surrounding nth element
     __a								=       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
     __a								=       [-1, 0, 1, -1, 1, -1, 0, 1]
     __a								=       True  # Make those cells visited
     for k in range(8   ):
       if self.is_safe(i + row_nbr[k]   ,				j + col_nbr[k]   ,				UpperCamelCase   ):
         self.diffs(i + row_nbr[k]   ,				j + col_nbr[k]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     int:  # And finally, count all islands.
     __a								=       [[False for j in range(self.COL   )] for i in range(self.ROW   )]
     __a								=       0
     for i in range(self.ROW   ):
       for j in range(self.COL   ):
         if visited[i][j] is False and self.graph[i][j] == 1:
           self.diffs(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
           count += 1
     return count



